import cog
import tempfile
from pathlib import Path
import argparse
import cv2
import torch
from PIL import Image
import torch.nn.functional as F
from torchvision import transforms
from models.models import create_model
from options.test_options import TestOptions
from util.reverse2original import reverse2wholeimage
from util.norm import SpecificNorm
from test_wholeimage_swapmulti import _totensor
from insightface_func.face_detect_crop_multi import Face_detect_crop as Face_detect_crop_multi
from insightface_func.face_detect_crop_single import Face_detect_crop as Face_detect_crop_single


class Predictor(cog.Predictor):
    def setup(self):
        self.transformer_Arcface = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    @cog.input("source", type=Path, help="source image")
    @cog.input("target", type=Path, help="target image")
    @cog.input("mode", type=str, options=['single', 'all'], default='all',
               help="swap a single face (the one with highest confidence by face detection) or all faces in the target image")
    def predict(self, source, target, mode='all'):

        app = Face_detect_crop_multi(name='antelope', root='./insightface_func/models')

        if mode == 'single':
            app = Face_detect_crop_single(name='antelope', root='./insightface_func/models')

        app.prepare(ctx_id=0, det_thresh=0.6, det_size=(640, 640))

        options = TestOptions()
        options.initialize()
        opt = options.parser.parse_args(["--Arc_path", 'arcface_model/arcface_checkpoint.tar', "--pic_a_path", str(source),
                                         "--pic_b_path", str(target), "--isTrain", False, "--no_simswaplogo"])

        str_ids = opt.gpu_ids.split(',')
        opt.gpu_ids = []
        for str_id in str_ids:
            id = int(str_id)
            if id >= 0:
                opt.gpu_ids.append(id)

        # set gpu ids
        if len(opt.gpu_ids) > 0:
            torch.cuda.set_device(opt.gpu_ids[0])

        torch.nn.Module.dump_patches = True
        model = create_model(opt)
        model.eval()

        crop_size = 224
        spNorm = SpecificNorm()

        with torch.no_grad():
            pic_a = opt.pic_a_path
            img_a_whole = cv2.imread(pic_a)
            img_a_align_crop, _ = app.get(img_a_whole, crop_size)
            img_a_align_crop_pil = Image.fromarray(cv2.cvtColor(img_a_align_crop[0], cv2.COLOR_BGR2RGB))
            img_a = self.transformer_Arcface(img_a_align_crop_pil)
            img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])

            # convert numpy to tensor
            img_id = img_id.cuda()

            # create latent id
            img_id_downsample = F.interpolate(img_id, scale_factor=0.5)
            latend_id = model.netArc(img_id_downsample)
            latend_id = F.normalize(latend_id, p=2, dim=1)

            ############## Forward Pass ######################

            pic_b = opt.pic_b_path
            img_b_whole = cv2.imread(pic_b)
            img_b_align_crop_list, b_mat_list = app.get(img_b_whole, crop_size)

            swap_result_list = []
            b_align_crop_tenor_list = []

            for b_align_crop in img_b_align_crop_list:
                b_align_crop_tenor = _totensor(cv2.cvtColor(b_align_crop, cv2.COLOR_BGR2RGB))[None, ...].cuda()

                swap_result = model(None, b_align_crop_tenor, latend_id, None, True)[0]
                swap_result_list.append(swap_result)
                b_align_crop_tenor_list.append(b_align_crop_tenor)

            net = None

            out_path = Path(tempfile.mkdtemp()) / "output.png"

            reverse2wholeimage(b_align_crop_tenor_list, swap_result_list, b_mat_list, crop_size, img_b_whole, None,
                               str(out_path), opt.no_simswaplogo,
                               pasring_model=net, use_mask=opt.use_mask, norm=spNorm)
            return out_path
